Setting Parameters:

Preprocessing performed with Seurat and Scater. Output to that Rmd file, will visualize multiple settings for PCs to use.

## Parameters for processing dataset and metadata ##
## Dimensionality reduction ##
# For PCA to run on
pcs_max = 70 
# PCs used for different UMAP representations
pcs_for_overview = c(5, 9, 10, 12, 16, 18, 20, 22, 25, 30, 35, 40, 43, 45, 50, 55)
lab_col = "Library"
# Chronological order of levels (for instance timepoints)
reorder_label <- "Timepoint"
order_levels <- c("d4" ,"d5","d7", "d8","d9", "d10","d11", "d12", "d13", "d14", "d18", "d21", "m5", "m11", "m12")

pc_set = 43
res_set = 2.3
umap_col = "Experiment"
umap_col2 = "Timepoint"
label.vector = c("Experiment", "Timepoint", "Lineage")

## Storing results ##
dataset_location <- "../scrna/CMdiff_mon-EB-EHT_combined/rerun_v4/preprocess_R/20211020_results_complete_regressed/seusetv3_scnormHVG_velocity.rds"
workdir <- "../scrna/CMdiff_mon-EB-EHT_combined/rerun_v4/preprocess_R/"
# if regression is performed: this will already be included in the folder name
result_descript = "_ExpIntegration"
system(paste("mkdir -p ", workdir))
knitr::opts_knit$set(root.dir = normalizePath(workdir))

Loading the dataset

Normalized, scaled dataset with information on the highest variable genes (HVGs).

seuset <- readRDS(paste0(dataset_location))

Dataset specific edit:

experiment_table <- read.table("../scrna/CMdiff_mon-EB-EHT_combined/sample-experiment_table.txt", header = TRUE, row.names = 1)
seuset@meta.data$Experiment <- experiment_table[seuset@meta.data$combined_id,]

Setting up results directory

dateoftoday <- gsub("-", "", as.character(Sys.Date()))
resultsdir <- paste(workdir, dateoftoday, result_descript, sep = "")
system(paste("mkdir -p ", resultsdir))
knitr::opts_knit$set(root.dir = normalizePath(resultsdir))

Results will be stored in: resultsdir

# split dataset into a list of seurat objects
seuset.list <- SplitObject(seuset, split.by = "Experiment")
# normalize and identify variable features for each dataset independently
seuset.list <- lapply(X = seuset.list, FUN = function(x) {
    x <- NormalizeData(x)
    x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
# select features that are repeatedly variable across datasets for integration
features <- SelectIntegrationFeatures(object.list = seuset.list)
seuset.anchors <- FindIntegrationAnchors(object.list = seuset.list, anchor.features = features)
## Scaling features for provided objects
## Finding all pairwise anchors
## Running CCA
## Merging objects
## Finding neighborhoods
## Finding anchors
##  Found 6622 anchors
## Filtering anchors
##  Retained 4189 anchors
## Running CCA
## Merging objects
## Finding neighborhoods
## Finding anchors
##  Found 2554 anchors
## Filtering anchors
##  Retained 1784 anchors
## Running CCA
## Merging objects
## Finding neighborhoods
## Finding anchors
##  Found 2729 anchors
## Filtering anchors
##  Retained 2131 anchors
## Running CCA
## Merging objects
## Finding neighborhoods
## Finding anchors
##  Found 6566 anchors
## Filtering anchors
##  Retained 2782 anchors
## Running CCA
## Merging objects
## Finding neighborhoods
## Finding anchors
##  Found 10052 anchors
## Filtering anchors
##  Retained 4989 anchors
## Running CCA
## Merging objects
## Finding neighborhoods
## Finding anchors
##  Found 2707 anchors
## Filtering anchors
##  Retained 679 anchors
seuset.combined <- IntegrateData(anchorset = seuset.anchors)
## Merging dataset 3 into 2
## Extracting anchors for merged samples
## Finding integration vectors
## Finding integration vector weights
## Integrating data
## Merging dataset 1 into 2 3
## Extracting anchors for merged samples
## Finding integration vectors
## Finding integration vector weights
## Integrating data
## Merging dataset 4 into 2 3 1
## Extracting anchors for merged samples
## Finding integration vectors
## Finding integration vector weights
## Integrating data
# specify that we will perform downstream analysis on the corrected data note that the original
# unmodified data still resides in the 'RNA' assay
DefaultAssay(seuset.combined) <- "integrated"

# Run the standard workflow for visualization and clustering
seuset.combined <- ScaleData(seuset.combined, verbose = FALSE)
seuset.combined <- RunPCA(seuset.combined, npcs = 70, verbose = FALSE)
ElbowPlot(seuset.combined, ndims = pcs_max)

seuset.combined <- RunUMAP(seuset.combined, reduction = "pca", dims = 1:30)
## 17:19:37 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:19:37 Read 12706 rows and found 30 numeric columns
## 17:19:37 Using Annoy for neighbor search, n_neighbors = 30
## 17:19:37 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:19:38 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b40189c871b
## 17:19:38 Searching Annoy index using 1 thread, search_k = 3000
## 17:19:41 Annoy recall = 100%
## 17:19:41 Commencing smooth kNN distance calibration using 1 thread
## 17:19:42 Initializing from normalized Laplacian + noise
## 17:19:42 Commencing optimization for 200 epochs, with 541210 positive edges
## 17:19:48 Optimization finished
seuset.combined <- FindNeighbors(seuset.combined, reduction = "pca", dims = 1:30)
## Computing nearest neighbor graph
## Computing SNN
seuset.combined <- FindClusters(seuset.combined, resolution = 1.5)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 12706
## Number of edges: 489989
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8571
## Number of communities: 32
## Elapsed time: 1 seconds

Adjusting set-up of the object

seuset.combined@meta.data[[reorder_label]] <- factor(seuset.combined@meta.data[[reorder_label]], levels = order_levels)
# Visualization
DimPlot(seuset.combined, reduction = "umap", group.by = "Timepoint")

DimPlot(seuset.combined, reduction = "umap", label = TRUE, repel = TRUE)

DimPlot(seuset.combined, reduction = "umap", group.by = "Experiment")

DimPlot(seuset.combined, reduction = "umap", group.by = "Lineage")

DimPlot(seuset.combined, reduction = "umap", group.by = "Method")

FeaturePlot(seuset.combined, c("COL3A1", "MYL7", "MYL2"))

Overview of different UMAPs with varying dimensional input

seuset <- seuset.combined

Visualizing PCA results:

#PrintPCA(object = seuset.scnorm, pcs.print = 1:5, genes.print = 5, use.full = FALSE)

VizDimLoadings(object = seuset, dims = 1:10, reduction = "pca")

VizDimLoadings(object = seuset, dims = 10:20, reduction = "pca")

pdf(paste0("VizPCAplot_PCs1-", pcs_max, ".pdf"), width = 20, height = 60)
VizDimLoadings(object = seuset, dims = 1:pcs_max, reduction = "pca")
dev.off()
## png 
##   2
DimPlot(object = seuset, reduction = "pca", group.by = lab_col)

# Helping in choosing the PCs to include in the analysis
DimHeatmap(
    object = seuset, 
    dims = 1:5, 
    cells = 500, 
    balanced = TRUE
)

pdf(paste0("PCheatmap_PCs1-", pcs_max, ".pdf"), width = 20, height = 60)
DimHeatmap(
    object = seuset, 
    dims = 1:pcs_max, 
    cells = 500, 
    balanced = TRUE
)
dev.off()
## png 
##   2

Overview of different UMAPs with varying dimensional input

The combined UMAP overview with various PC inputs (1-X, with X as each of the values in the defined pcs_for_overview variable), for a first insight into the variability of the dataset.

combine_umap_plot <- function(umap_label, pcs_for_overview){
  plot.list <- list()
  for (i in (1:length(pcs_for_overview))){
    seuset <- RunUMAP(seuset, dims = 1:pcs_for_overview[i])
    dimnr <- as.character(pcs_for_overview[i])
    print(dimnr)
    if (i == 1){
      plot.list[[dimnr]] <-
        DimPlot(seuset,
        reduction = "umap",
        group.by = umap_label,
        combine = TRUE) + ggtitle(paste0("UMAP 1:", dimnr))
    } else {
      plot.list[[dimnr]] <-
        DimPlot(seuset,
        reduction = "umap",
        group.by = umap_label,
        combine = TRUE) + ggtitle(paste0("UMAP 1:", dimnr)) + theme(legend.position = "none")
    }
  }
  #Generate combined plot for umap variabel
  return(plot.list)
}

#Apply to each defined umap component in label vector
for (umap_label in label.vector) {
  plot.list <- combine_umap_plot(umap_label, pcs_for_overview)
  pdf(
    paste0("UMAPdiffsettings_",
    paste(as.character(pcs_for_overview), collapse = "-"),
    "_",
    umap_label,
    ".pdf"
    ),
    width = 20,
    height = 15
    )
    print(CombinePlots(plot.list, nrows = round(length(pcs_for_overview)/3)))
  dev.off()
}
## 17:20:09 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:20:09 Read 12706 rows and found 5 numeric columns
## 17:20:09 Using Annoy for neighbor search, n_neighbors = 30
## 17:20:09 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:20:10 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b407875b5f1
## 17:20:10 Searching Annoy index using 1 thread, search_k = 3000
## 17:20:15 Annoy recall = 100%
## 17:20:15 Commencing smooth kNN distance calibration using 1 thread
## 17:20:16 Initializing from normalized Laplacian + noise
## 17:20:17 Commencing optimization for 200 epochs, with 473410 positive edges
## 17:20:21 Optimization finished
## [1] "5"
## 17:20:22 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:20:22 Read 12706 rows and found 9 numeric columns
## 17:20:22 Using Annoy for neighbor search, n_neighbors = 30
## 17:20:22 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:20:23 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b4047ea0f84
## 17:20:23 Searching Annoy index using 1 thread, search_k = 3000
## 17:20:27 Annoy recall = 100%
## 17:20:27 Commencing smooth kNN distance calibration using 1 thread
## 17:20:28 Initializing from normalized Laplacian + noise
## 17:20:28 Commencing optimization for 200 epochs, with 508302 positive edges
## 17:20:33 Optimization finished
## [1] "9"
## 17:20:33 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:20:33 Read 12706 rows and found 10 numeric columns
## 17:20:33 Using Annoy for neighbor search, n_neighbors = 30
## 17:20:33 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:20:34 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b40416f4854
## 17:20:34 Searching Annoy index using 1 thread, search_k = 3000
## 17:20:38 Annoy recall = 100%
## 17:20:39 Commencing smooth kNN distance calibration using 1 thread
## 17:20:39 Initializing from normalized Laplacian + noise
## 17:20:40 Commencing optimization for 200 epochs, with 514308 positive edges
## 17:20:45 Optimization finished
## [1] "10"
## 17:20:45 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:20:45 Read 12706 rows and found 12 numeric columns
## 17:20:45 Using Annoy for neighbor search, n_neighbors = 30
## 17:20:45 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:20:46 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b403a3f67c1
## 17:20:46 Searching Annoy index using 1 thread, search_k = 3000
## 17:20:50 Annoy recall = 100%
## 17:20:50 Commencing smooth kNN distance calibration using 1 thread
## 17:20:51 Initializing from normalized Laplacian + noise
## 17:20:51 Commencing optimization for 200 epochs, with 518994 positive edges
## 17:20:56 Optimization finished
## [1] "12"
## 17:20:56 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:20:56 Read 12706 rows and found 16 numeric columns
## 17:20:56 Using Annoy for neighbor search, n_neighbors = 30
## 17:20:56 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:20:57 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b4069791641
## 17:20:57 Searching Annoy index using 1 thread, search_k = 3000
## 17:21:01 Annoy recall = 100%
## 17:21:01 Commencing smooth kNN distance calibration using 1 thread
## 17:21:02 Initializing from normalized Laplacian + noise
## 17:21:02 Commencing optimization for 200 epochs, with 526352 positive edges
## 17:21:07 Optimization finished
## [1] "16"
## 17:21:08 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:21:08 Read 12706 rows and found 18 numeric columns
## 17:21:08 Using Annoy for neighbor search, n_neighbors = 30
## 17:21:08 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:21:09 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b403c77e514
## 17:21:09 Searching Annoy index using 1 thread, search_k = 3000
## 17:21:12 Annoy recall = 100%
## 17:21:12 Commencing smooth kNN distance calibration using 1 thread
## 17:21:13 Initializing from normalized Laplacian + noise
## 17:21:13 Commencing optimization for 200 epochs, with 528346 positive edges
## 17:21:19 Optimization finished
## [1] "18"
## 17:21:19 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:21:19 Read 12706 rows and found 20 numeric columns
## 17:21:19 Using Annoy for neighbor search, n_neighbors = 30
## 17:21:19 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:21:20 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b401d4351cf
## 17:21:20 Searching Annoy index using 1 thread, search_k = 3000
## 17:21:23 Annoy recall = 100%
## 17:21:23 Commencing smooth kNN distance calibration using 1 thread
## 17:21:24 Initializing from normalized Laplacian + noise
## 17:21:24 Commencing optimization for 200 epochs, with 531882 positive edges
## 17:21:30 Optimization finished
## [1] "20"
## 17:21:30 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:21:30 Read 12706 rows and found 22 numeric columns
## 17:21:30 Using Annoy for neighbor search, n_neighbors = 30
## 17:21:30 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:21:31 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b4028c1a0d9
## 17:21:31 Searching Annoy index using 1 thread, search_k = 3000
## 17:21:34 Annoy recall = 100%
## 17:21:34 Commencing smooth kNN distance calibration using 1 thread
## 17:21:35 Initializing from normalized Laplacian + noise
## 17:21:35 Commencing optimization for 200 epochs, with 533798 positive edges
## 17:21:41 Optimization finished
## [1] "22"
## 17:21:41 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:21:41 Read 12706 rows and found 25 numeric columns
## 17:21:41 Using Annoy for neighbor search, n_neighbors = 30
## 17:21:41 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:21:42 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b40738897a0
## 17:21:42 Searching Annoy index using 1 thread, search_k = 3000
## 17:21:45 Annoy recall = 100%
## 17:21:45 Commencing smooth kNN distance calibration using 1 thread
## 17:21:46 Initializing from normalized Laplacian + noise
## 17:21:46 Commencing optimization for 200 epochs, with 536852 positive edges
## 17:21:52 Optimization finished
## [1] "25"
## 17:21:52 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:21:52 Read 12706 rows and found 30 numeric columns
## 17:21:52 Using Annoy for neighbor search, n_neighbors = 30
## 17:21:52 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:21:53 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b405130d7c6
## 17:21:53 Searching Annoy index using 1 thread, search_k = 3000
## 17:21:56 Annoy recall = 100%
## 17:21:56 Commencing smooth kNN distance calibration using 1 thread
## 17:21:57 Initializing from normalized Laplacian + noise
## 17:21:57 Commencing optimization for 200 epochs, with 541210 positive edges
## 17:22:03 Optimization finished
## [1] "30"
## 17:22:03 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:22:03 Read 12706 rows and found 35 numeric columns
## 17:22:03 Using Annoy for neighbor search, n_neighbors = 30
## 17:22:03 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:22:04 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b40608f8b8b
## 17:22:04 Searching Annoy index using 1 thread, search_k = 3000
## 17:22:07 Annoy recall = 100%
## 17:22:07 Commencing smooth kNN distance calibration using 1 thread
## 17:22:08 Initializing from normalized Laplacian + noise
## 17:22:08 Commencing optimization for 200 epochs, with 544892 positive edges
## 17:22:14 Optimization finished
## [1] "35"
## 17:22:14 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:22:14 Read 12706 rows and found 40 numeric columns
## 17:22:14 Using Annoy for neighbor search, n_neighbors = 30
## 17:22:14 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:22:15 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b407f0427b7
## 17:22:15 Searching Annoy index using 1 thread, search_k = 3000
## 17:22:18 Annoy recall = 100%
## 17:22:18 Commencing smooth kNN distance calibration using 1 thread
## 17:22:19 Initializing from normalized Laplacian + noise
## 17:22:19 Commencing optimization for 200 epochs, with 549032 positive edges
## 17:22:24 Optimization finished
## [1] "40"
## 17:22:24 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:22:24 Read 12706 rows and found 43 numeric columns
## 17:22:24 Using Annoy for neighbor search, n_neighbors = 30
## 17:22:24 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:22:26 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b402ee14ad0
## 17:22:26 Searching Annoy index using 1 thread, search_k = 3000
## 17:22:29 Annoy recall = 100%
## 17:22:29 Commencing smooth kNN distance calibration using 1 thread
## 17:22:30 Initializing from normalized Laplacian + noise
## 17:22:30 Commencing optimization for 200 epochs, with 550748 positive edges
## 17:22:35 Optimization finished
## [1] "43"
## 17:22:36 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:22:36 Read 12706 rows and found 45 numeric columns
## 17:22:36 Using Annoy for neighbor search, n_neighbors = 30
## 17:22:36 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:22:37 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b40133fe05
## 17:22:37 Searching Annoy index using 1 thread, search_k = 3000
## 17:22:40 Annoy recall = 100%
## 17:22:40 Commencing smooth kNN distance calibration using 1 thread
## 17:22:41 Initializing from normalized Laplacian + noise
## 17:22:41 Commencing optimization for 200 epochs, with 552206 positive edges
## 17:22:46 Optimization finished
## [1] "45"
## 17:22:46 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:22:46 Read 12706 rows and found 50 numeric columns
## 17:22:46 Using Annoy for neighbor search, n_neighbors = 30
## 17:22:46 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:22:48 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b406604d166
## 17:22:48 Searching Annoy index using 1 thread, search_k = 3000
## 17:22:51 Annoy recall = 100%
## 17:22:51 Commencing smooth kNN distance calibration using 1 thread
## 17:22:52 Initializing from normalized Laplacian + noise
## 17:22:52 Commencing optimization for 200 epochs, with 555434 positive edges
## 17:22:57 Optimization finished
## [1] "50"
## 17:22:58 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:22:58 Read 12706 rows and found 55 numeric columns
## 17:22:58 Using Annoy for neighbor search, n_neighbors = 30
## 17:22:58 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:22:59 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b4024730856
## 17:22:59 Searching Annoy index using 1 thread, search_k = 3000
## 17:23:02 Annoy recall = 100%
## 17:23:02 Commencing smooth kNN distance calibration using 1 thread
## 17:23:03 Initializing from normalized Laplacian + noise
## 17:23:03 Commencing optimization for 200 epochs, with 558416 positive edges
## 17:23:08 Optimization finished
## [1] "55"
## 17:23:15 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:23:15 Read 12706 rows and found 5 numeric columns
## 17:23:15 Using Annoy for neighbor search, n_neighbors = 30
## 17:23:15 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:23:17 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b4050a0b650
## 17:23:17 Searching Annoy index using 1 thread, search_k = 3000
## 17:23:22 Annoy recall = 100%
## 17:23:22 Commencing smooth kNN distance calibration using 1 thread
## 17:23:23 Initializing from normalized Laplacian + noise
## 17:23:23 Commencing optimization for 200 epochs, with 473410 positive edges
## 17:23:28 Optimization finished
## [1] "5"
## 17:23:28 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:23:28 Read 12706 rows and found 9 numeric columns
## 17:23:28 Using Annoy for neighbor search, n_neighbors = 30
## 17:23:28 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:23:29 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b4027295723
## 17:23:29 Searching Annoy index using 1 thread, search_k = 3000
## 17:23:33 Annoy recall = 100%
## 17:23:33 Commencing smooth kNN distance calibration using 1 thread
## 17:23:34 Initializing from normalized Laplacian + noise
## 17:23:34 Commencing optimization for 200 epochs, with 508302 positive edges
## 17:23:40 Optimization finished
## [1] "9"
## 17:23:40 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:23:40 Read 12706 rows and found 10 numeric columns
## 17:23:40 Using Annoy for neighbor search, n_neighbors = 30
## 17:23:40 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:23:41 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b40209e9ad8
## 17:23:41 Searching Annoy index using 1 thread, search_k = 3000
## 17:23:45 Annoy recall = 100%
## 17:23:45 Commencing smooth kNN distance calibration using 1 thread
## 17:23:46 Initializing from normalized Laplacian + noise
## 17:23:46 Commencing optimization for 200 epochs, with 514308 positive edges
## 17:23:51 Optimization finished
## [1] "10"
## 17:23:51 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:23:51 Read 12706 rows and found 12 numeric columns
## 17:23:51 Using Annoy for neighbor search, n_neighbors = 30
## 17:23:51 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:23:52 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b404a514e0c
## 17:23:52 Searching Annoy index using 1 thread, search_k = 3000
## 17:23:56 Annoy recall = 100%
## 17:23:56 Commencing smooth kNN distance calibration using 1 thread
## 17:23:57 Initializing from normalized Laplacian + noise
## 17:23:57 Commencing optimization for 200 epochs, with 518994 positive edges
## 17:24:02 Optimization finished
## [1] "12"
## 17:24:02 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:24:02 Read 12706 rows and found 16 numeric columns
## 17:24:02 Using Annoy for neighbor search, n_neighbors = 30
## 17:24:02 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:24:03 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b402be2fe28
## 17:24:03 Searching Annoy index using 1 thread, search_k = 3000
## 17:24:07 Annoy recall = 100%
## 17:24:07 Commencing smooth kNN distance calibration using 1 thread
## 17:24:08 Initializing from normalized Laplacian + noise
## 17:24:08 Commencing optimization for 200 epochs, with 526352 positive edges
## 17:24:14 Optimization finished
## [1] "16"
## 17:24:14 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:24:14 Read 12706 rows and found 18 numeric columns
## 17:24:14 Using Annoy for neighbor search, n_neighbors = 30
## 17:24:14 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:24:15 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b4010af99b0
## 17:24:15 Searching Annoy index using 1 thread, search_k = 3000
## 17:24:18 Annoy recall = 100%
## 17:24:18 Commencing smooth kNN distance calibration using 1 thread
## 17:24:19 Initializing from normalized Laplacian + noise
## 17:24:19 Commencing optimization for 200 epochs, with 528346 positive edges
## 17:24:25 Optimization finished
## [1] "18"
## 17:24:25 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:24:25 Read 12706 rows and found 20 numeric columns
## 17:24:25 Using Annoy for neighbor search, n_neighbors = 30
## 17:24:25 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:24:26 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b4062edd527
## 17:24:26 Searching Annoy index using 1 thread, search_k = 3000
## 17:24:29 Annoy recall = 100%
## 17:24:29 Commencing smooth kNN distance calibration using 1 thread
## 17:24:30 Initializing from normalized Laplacian + noise
## 17:24:30 Commencing optimization for 200 epochs, with 531882 positive edges
## 17:24:36 Optimization finished
## [1] "20"
## 17:24:36 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:24:36 Read 12706 rows and found 22 numeric columns
## 17:24:36 Using Annoy for neighbor search, n_neighbors = 30
## 17:24:36 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:24:37 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b4037233334
## 17:24:37 Searching Annoy index using 1 thread, search_k = 3000
## 17:24:40 Annoy recall = 100%
## 17:24:40 Commencing smooth kNN distance calibration using 1 thread
## 17:24:41 Initializing from normalized Laplacian + noise
## 17:24:41 Commencing optimization for 200 epochs, with 533798 positive edges
## 17:24:47 Optimization finished
## [1] "22"
## 17:24:47 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:24:47 Read 12706 rows and found 25 numeric columns
## 17:24:47 Using Annoy for neighbor search, n_neighbors = 30
## 17:24:47 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:24:48 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b4032cd1cae
## 17:24:48 Searching Annoy index using 1 thread, search_k = 3000
## 17:24:51 Annoy recall = 100%
## 17:24:51 Commencing smooth kNN distance calibration using 1 thread
## 17:24:52 Initializing from normalized Laplacian + noise
## 17:24:52 Commencing optimization for 200 epochs, with 536852 positive edges
## 17:24:58 Optimization finished
## [1] "25"
## 17:24:58 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:24:58 Read 12706 rows and found 30 numeric columns
## 17:24:58 Using Annoy for neighbor search, n_neighbors = 30
## 17:24:58 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:24:59 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b407e3db2d6
## 17:24:59 Searching Annoy index using 1 thread, search_k = 3000
## 17:25:02 Annoy recall = 100%
## 17:25:02 Commencing smooth kNN distance calibration using 1 thread
## 17:25:03 Initializing from normalized Laplacian + noise
## 17:25:03 Commencing optimization for 200 epochs, with 541210 positive edges
## 17:25:09 Optimization finished
## [1] "30"
## 17:25:09 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:25:09 Read 12706 rows and found 35 numeric columns
## 17:25:09 Using Annoy for neighbor search, n_neighbors = 30
## 17:25:09 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:25:10 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b403a6c1e56
## 17:25:10 Searching Annoy index using 1 thread, search_k = 3000
## 17:25:13 Annoy recall = 100%
## 17:25:13 Commencing smooth kNN distance calibration using 1 thread
## 17:25:14 Initializing from normalized Laplacian + noise
## 17:25:14 Commencing optimization for 200 epochs, with 544892 positive edges
## 17:25:20 Optimization finished
## [1] "35"
## 17:25:20 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:25:20 Read 12706 rows and found 40 numeric columns
## 17:25:20 Using Annoy for neighbor search, n_neighbors = 30
## 17:25:20 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:25:21 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b401368be22
## 17:25:21 Searching Annoy index using 1 thread, search_k = 3000
## 17:25:24 Annoy recall = 100%
## 17:25:24 Commencing smooth kNN distance calibration using 1 thread
## 17:25:25 Initializing from normalized Laplacian + noise
## 17:25:25 Commencing optimization for 200 epochs, with 549032 positive edges
## 17:25:31 Optimization finished
## [1] "40"
## 17:25:31 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:25:31 Read 12706 rows and found 43 numeric columns
## 17:25:31 Using Annoy for neighbor search, n_neighbors = 30
## 17:25:31 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:25:32 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b402748881
## 17:25:32 Searching Annoy index using 1 thread, search_k = 3000
## 17:25:35 Annoy recall = 100%
## 17:25:35 Commencing smooth kNN distance calibration using 1 thread
## 17:25:36 Initializing from normalized Laplacian + noise
## 17:25:36 Commencing optimization for 200 epochs, with 550748 positive edges
## 17:25:42 Optimization finished
## [1] "43"
## 17:25:42 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:25:42 Read 12706 rows and found 45 numeric columns
## 17:25:42 Using Annoy for neighbor search, n_neighbors = 30
## 17:25:42 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:25:43 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b40554f1773
## 17:25:43 Searching Annoy index using 1 thread, search_k = 3000
## 17:25:46 Annoy recall = 100%
## 17:25:46 Commencing smooth kNN distance calibration using 1 thread
## 17:25:47 Initializing from normalized Laplacian + noise
## 17:25:47 Commencing optimization for 200 epochs, with 552206 positive edges
## 17:25:53 Optimization finished
## [1] "45"
## 17:25:53 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:25:53 Read 12706 rows and found 50 numeric columns
## 17:25:53 Using Annoy for neighbor search, n_neighbors = 30
## 17:25:53 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:25:54 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b40bde7413
## 17:25:54 Searching Annoy index using 1 thread, search_k = 3000
## 17:25:57 Annoy recall = 100%
## 17:25:58 Commencing smooth kNN distance calibration using 1 thread
## 17:25:58 Initializing from normalized Laplacian + noise
## 17:25:58 Commencing optimization for 200 epochs, with 555434 positive edges
## 17:26:04 Optimization finished
## [1] "50"
## 17:26:04 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:26:04 Read 12706 rows and found 55 numeric columns
## 17:26:04 Using Annoy for neighbor search, n_neighbors = 30
## 17:26:04 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:26:05 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b404a5e9805
## 17:26:05 Searching Annoy index using 1 thread, search_k = 3000
## 17:26:08 Annoy recall = 100%
## 17:26:09 Commencing smooth kNN distance calibration using 1 thread
## 17:26:09 Initializing from normalized Laplacian + noise
## 17:26:10 Commencing optimization for 200 epochs, with 558416 positive edges
## 17:26:15 Optimization finished
## [1] "55"
## 17:26:22 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:26:22 Read 12706 rows and found 5 numeric columns
## 17:26:22 Using Annoy for neighbor search, n_neighbors = 30
## 17:26:22 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:26:24 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b40461ddbd5
## 17:26:24 Searching Annoy index using 1 thread, search_k = 3000
## 17:26:28 Annoy recall = 100%
## 17:26:29 Commencing smooth kNN distance calibration using 1 thread
## 17:26:29 Initializing from normalized Laplacian + noise
## 17:26:30 Commencing optimization for 200 epochs, with 473410 positive edges
## 17:26:35 Optimization finished
## [1] "5"
## 17:26:35 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:26:35 Read 12706 rows and found 9 numeric columns
## 17:26:35 Using Annoy for neighbor search, n_neighbors = 30
## 17:26:35 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:26:36 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b4033d7ae46
## 17:26:36 Searching Annoy index using 1 thread, search_k = 3000
## 17:26:40 Annoy recall = 100%
## 17:26:40 Commencing smooth kNN distance calibration using 1 thread
## 17:26:41 Initializing from normalized Laplacian + noise
## 17:26:41 Commencing optimization for 200 epochs, with 508302 positive edges
## 17:26:46 Optimization finished
## [1] "9"
## 17:26:46 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:26:46 Read 12706 rows and found 10 numeric columns
## 17:26:46 Using Annoy for neighbor search, n_neighbors = 30
## 17:26:46 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:26:47 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b40533644dc
## 17:26:47 Searching Annoy index using 1 thread, search_k = 3000
## 17:26:52 Annoy recall = 100%
## 17:26:52 Commencing smooth kNN distance calibration using 1 thread
## 17:26:52 Initializing from normalized Laplacian + noise
## 17:26:53 Commencing optimization for 200 epochs, with 514308 positive edges
## 17:26:58 Optimization finished
## [1] "10"
## 17:26:58 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:26:58 Read 12706 rows and found 12 numeric columns
## 17:26:58 Using Annoy for neighbor search, n_neighbors = 30
## 17:26:58 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:26:59 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b4063612da4
## 17:26:59 Searching Annoy index using 1 thread, search_k = 3000
## 17:27:03 Annoy recall = 100%
## 17:27:03 Commencing smooth kNN distance calibration using 1 thread
## 17:27:04 Initializing from normalized Laplacian + noise
## 17:27:04 Commencing optimization for 200 epochs, with 518994 positive edges
## 17:27:09 Optimization finished
## [1] "12"
## 17:27:09 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:27:09 Read 12706 rows and found 16 numeric columns
## 17:27:09 Using Annoy for neighbor search, n_neighbors = 30
## 17:27:09 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:27:10 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b405c994f1f
## 17:27:10 Searching Annoy index using 1 thread, search_k = 3000
## 17:27:14 Annoy recall = 100%
## 17:27:14 Commencing smooth kNN distance calibration using 1 thread
## 17:27:15 Initializing from normalized Laplacian + noise
## 17:27:15 Commencing optimization for 200 epochs, with 526352 positive edges
## 17:27:20 Optimization finished
## [1] "16"
## 17:27:21 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:27:21 Read 12706 rows and found 18 numeric columns
## 17:27:21 Using Annoy for neighbor search, n_neighbors = 30
## 17:27:21 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:27:21 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b4046bedc7c
## 17:27:22 Searching Annoy index using 1 thread, search_k = 3000
## 17:27:25 Annoy recall = 100%
## 17:27:25 Commencing smooth kNN distance calibration using 1 thread
## 17:27:26 Initializing from normalized Laplacian + noise
## 17:27:26 Commencing optimization for 200 epochs, with 528346 positive edges
## 17:27:32 Optimization finished
## [1] "18"
## 17:27:32 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:27:32 Read 12706 rows and found 20 numeric columns
## 17:27:32 Using Annoy for neighbor search, n_neighbors = 30
## 17:27:32 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:27:33 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b403492056a
## 17:27:33 Searching Annoy index using 1 thread, search_k = 3000
## 17:27:36 Annoy recall = 100%
## 17:27:36 Commencing smooth kNN distance calibration using 1 thread
## 17:27:37 Initializing from normalized Laplacian + noise
## 17:27:37 Commencing optimization for 200 epochs, with 531882 positive edges
## 17:27:43 Optimization finished
## [1] "20"
## 17:27:43 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:27:43 Read 12706 rows and found 22 numeric columns
## 17:27:43 Using Annoy for neighbor search, n_neighbors = 30
## 17:27:43 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:27:44 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b403d28daaa
## 17:27:44 Searching Annoy index using 1 thread, search_k = 3000
## 17:27:47 Annoy recall = 100%
## 17:27:47 Commencing smooth kNN distance calibration using 1 thread
## 17:27:48 Initializing from normalized Laplacian + noise
## 17:27:48 Commencing optimization for 200 epochs, with 533798 positive edges
## 17:27:54 Optimization finished
## [1] "22"
## 17:27:54 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:27:54 Read 12706 rows and found 25 numeric columns
## 17:27:54 Using Annoy for neighbor search, n_neighbors = 30
## 17:27:54 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:27:55 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b4045c30434
## 17:27:55 Searching Annoy index using 1 thread, search_k = 3000
## 17:27:58 Annoy recall = 100%
## 17:27:58 Commencing smooth kNN distance calibration using 1 thread
## 17:27:59 Initializing from normalized Laplacian + noise
## 17:27:59 Commencing optimization for 200 epochs, with 536852 positive edges
## 17:28:05 Optimization finished
## [1] "25"
## 17:28:05 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:28:05 Read 12706 rows and found 30 numeric columns
## 17:28:05 Using Annoy for neighbor search, n_neighbors = 30
## 17:28:05 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:28:06 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b406373503a
## 17:28:06 Searching Annoy index using 1 thread, search_k = 3000
## 17:28:09 Annoy recall = 100%
## 17:28:09 Commencing smooth kNN distance calibration using 1 thread
## 17:28:10 Initializing from normalized Laplacian + noise
## 17:28:10 Commencing optimization for 200 epochs, with 541210 positive edges
## 17:28:16 Optimization finished
## [1] "30"
## 17:28:16 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:28:16 Read 12706 rows and found 35 numeric columns
## 17:28:16 Using Annoy for neighbor search, n_neighbors = 30
## 17:28:16 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:28:17 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b403e5cd8af
## 17:28:17 Searching Annoy index using 1 thread, search_k = 3000
## 17:28:20 Annoy recall = 100%
## 17:28:20 Commencing smooth kNN distance calibration using 1 thread
## 17:28:21 Initializing from normalized Laplacian + noise
## 17:28:21 Commencing optimization for 200 epochs, with 544892 positive edges
## 17:28:27 Optimization finished
## [1] "35"
## 17:28:27 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:28:27 Read 12706 rows and found 40 numeric columns
## 17:28:27 Using Annoy for neighbor search, n_neighbors = 30
## 17:28:27 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:28:28 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b402bc7d59a
## 17:28:28 Searching Annoy index using 1 thread, search_k = 3000
## 17:28:31 Annoy recall = 100%
## 17:28:31 Commencing smooth kNN distance calibration using 1 thread
## 17:28:32 Initializing from normalized Laplacian + noise
## 17:28:32 Commencing optimization for 200 epochs, with 549032 positive edges
## 17:28:38 Optimization finished
## [1] "40"
## 17:28:38 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:28:38 Read 12706 rows and found 43 numeric columns
## 17:28:38 Using Annoy for neighbor search, n_neighbors = 30
## 17:28:38 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:28:39 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b407e65890
## 17:28:39 Searching Annoy index using 1 thread, search_k = 3000
## 17:28:42 Annoy recall = 100%
## 17:28:42 Commencing smooth kNN distance calibration using 1 thread
## 17:28:43 Initializing from normalized Laplacian + noise
## 17:28:43 Commencing optimization for 200 epochs, with 550748 positive edges
## 17:28:49 Optimization finished
## [1] "43"
## 17:28:49 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:28:49 Read 12706 rows and found 45 numeric columns
## 17:28:49 Using Annoy for neighbor search, n_neighbors = 30
## 17:28:49 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:28:50 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b4072d8b6c5
## 17:28:50 Searching Annoy index using 1 thread, search_k = 3000
## 17:28:53 Annoy recall = 100%
## 17:28:53 Commencing smooth kNN distance calibration using 1 thread
## 17:28:54 Initializing from normalized Laplacian + noise
## 17:28:54 Commencing optimization for 200 epochs, with 552206 positive edges
## 17:29:00 Optimization finished
## [1] "45"
## 17:29:00 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:29:00 Read 12706 rows and found 50 numeric columns
## 17:29:00 Using Annoy for neighbor search, n_neighbors = 30
## 17:29:00 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:29:01 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b407c688bea
## 17:29:01 Searching Annoy index using 1 thread, search_k = 3000
## 17:29:04 Annoy recall = 100%
## 17:29:04 Commencing smooth kNN distance calibration using 1 thread
## 17:29:05 Initializing from normalized Laplacian + noise
## 17:29:05 Commencing optimization for 200 epochs, with 555434 positive edges
## 17:29:11 Optimization finished
## [1] "50"
## 17:29:11 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:29:11 Read 12706 rows and found 55 numeric columns
## 17:29:11 Using Annoy for neighbor search, n_neighbors = 30
## 17:29:11 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:29:12 Writing NN index file to temp file /scratch/snabel/tmp/BoiX/Rtmp01Usjr/file22b402f0fafb3
## 17:29:12 Searching Annoy index using 1 thread, search_k = 3000
## 17:29:15 Annoy recall = 100%
## 17:29:16 Commencing smooth kNN distance calibration using 1 thread
## 17:29:16 Initializing from normalized Laplacian + noise
## 17:29:16 Commencing optimization for 200 epochs, with 558416 positive edges
## 17:29:22 Optimization finished
## [1] "55"
# Saving the dataset with the normalized, scaled and identified HVGs (stored in seuset.scnorm@var.genes).
saveRDS(seuset, file="seusetv3_integrated.rds")
sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS/LAPACK: /vol/mbconda/snabel/anaconda3/envs/kb_scrna_R_mon3/lib/libopenblasp-r0.3.17.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] patchwork_1.1.1    Matrix_1.3-4       scales_1.1.1       RColorBrewer_1.1-2
##  [5] SeuratObject_4.0.2 Seurat_4.0.4       knitr_1.33         tidyr_1.1.3       
##  [9] dplyr_1.0.7        ggplot2_3.3.5     
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.15            colorspace_2.0-2      deldir_0.2-10        
##   [4] ellipsis_0.3.2        ggridges_0.5.3        spatstat.data_2.1-0  
##   [7] farver_2.1.0          leiden_0.3.9          listenv_0.8.0        
##  [10] ggrepel_0.9.1         RSpectra_0.16-0       fansi_0.5.0          
##  [13] codetools_0.2-18      splines_4.1.0         polyclip_1.10-0      
##  [16] jsonlite_1.7.2        ica_1.0-2             cluster_2.1.2        
##  [19] png_0.1-7             uwot_0.1.10           shiny_1.6.0          
##  [22] sctransform_0.3.2     spatstat.sparse_2.0-0 compiler_4.1.0       
##  [25] httr_1.4.2            assertthat_0.2.1      fastmap_1.1.0        
##  [28] lazyeval_0.2.2        cli_3.0.1             limma_3.48.3         
##  [31] later_1.3.0           htmltools_0.5.2       tools_4.1.0          
##  [34] igraph_1.2.6          gtable_0.3.0          glue_1.4.2           
##  [37] RANN_2.6.1            reshape2_1.4.4        Rcpp_1.0.7           
##  [40] scattermore_0.7       jquerylib_0.1.4       vctrs_0.3.8          
##  [43] nlme_3.1-152          lmtest_0.9-38         xfun_0.25            
##  [46] stringr_1.4.0         globals_0.14.0        mime_0.11            
##  [49] miniUI_0.1.1.1        lifecycle_1.0.0       irlba_2.3.3          
##  [52] goftest_1.2-2         future_1.22.1         MASS_7.3-54          
##  [55] zoo_1.8-9             spatstat.core_2.3-0   promises_1.2.0.1     
##  [58] spatstat.utils_2.2-0  parallel_4.1.0        yaml_2.2.1           
##  [61] reticulate_1.20       pbapply_1.4-3         gridExtra_2.3        
##  [64] sass_0.4.0            rpart_4.1-15          stringi_1.7.4        
##  [67] highr_0.9             rlang_0.4.11          pkgconfig_2.0.3      
##  [70] matrixStats_0.60.1    evaluate_0.14         lattice_0.20-44      
##  [73] ROCR_1.0-11           purrr_0.3.4           tensor_1.5           
##  [76] labeling_0.4.2        htmlwidgets_1.5.3     cowplot_1.1.1        
##  [79] tidyselect_1.1.1      parallelly_1.27.0     RcppAnnoy_0.0.19     
##  [82] plyr_1.8.6            magrittr_2.0.1        R6_2.5.1             
##  [85] generics_0.1.0        DBI_1.1.1             pillar_1.6.2         
##  [88] withr_2.4.2           mgcv_1.8-36           fitdistrplus_1.1-5   
##  [91] survival_3.2-13       abind_1.4-5           tibble_3.1.4         
##  [94] future.apply_1.8.1    crayon_1.4.1          KernSmooth_2.23-20   
##  [97] utf8_1.2.2            spatstat.geom_2.2-2   plotly_4.9.4.1       
## [100] rmarkdown_2.10        grid_4.1.0            data.table_1.14.0    
## [103] digest_0.6.27         xtable_1.8-4          httpuv_1.6.2         
## [106] munsell_0.5.0         viridisLite_0.4.0     bslib_0.3.0